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yueyulin/
entity_type_extraction

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This is a state for rwkv6_7b_v2.1 that generates entity_types given domain, expert role in this domain and specific tasks that this export can do that are generated from persona_domain agent.

The input is solely the context that you want this model to analyze
The output are domain, expert role in this domain and specific tasks that this export can do in a jsonl format.

Please refer to the following demo as test code:

python

from rwkv.model import RWKV
from rwkv.utils import PIPELINE, PIPELINE_ARGS
import torch

# download models: https://huggingface.co/BlinkDL
model = RWKV(model='/home/rwkv/Peter/model/base/RWKV-x060-World-7B-v2.1-20240507-ctx4096.pth', strategy='cuda fp16')
print(model.args)
pipeline = PIPELINE(model, "rwkv_vocab_v20230424") # 20B_tokenizer.json is in https://github.com/BlinkDL/ChatRWKV
# use pipeline = PIPELINE(model, "rwkv_vocab_v20230424") for rwkv "world" models
states_file = '/home/rwkv/Peter/rwkv_graphrag/agents/persona_domain_states/RWKV-x060-World-7B-v2.1-20240507-ctx4096.pth.pth'
states = torch.load(states_file)
states_value = []
device = 'cuda'
n_head = model.args.n_head
head_size = model.args.n_embd//model.args.n_head
for i in range(model.args.n_layer):
    key = f'blocks.{i}.att.time_state'
    value = states[key]
    prev_x = torch.zeros(model.args.n_embd,device=device,dtype=torch.float16)
    prev_states = value.clone().detach().to(device=device,dtype=torch.float16).transpose(1,2)
    prev_ffn = torch.zeros(model.args.n_embd,device=device,dtype=torch.float16)
    states_value.append(prev_x)
    states_value.append(prev_states)
    states_value.append(prev_ffn)

cat_char = '🐱'
bot_char = '🤖'
instruction ='根据input中的领域和任务，协助用户识别input文本中存在的实体类型。 实体类型必须与用户任务相关。 避免使用诸如“其他”或“未知”的通用实体类型。 非常重要的是：不要生成冗余或重叠的实体类型。用JSON格式输出。'
input_text = '{"领域": "文学与神话", "专家": "文学史学者/神话学家", "任务": ["分析《石头记》的历史背景和影响", "研究《红楼梦》与《金陵十二钗》之间的关系", "探讨东鲁孔梅溪对《石头记》的改编过程", "解析吴玉峰在《红楼梦》中的角色和贡献", "评估曹雪芹在《悼红轩中披阅十五间》中的写作技巧"]}'
ctx = f'{cat_char}:{instruction}\n{input_text}\n{bot_char}:'
print(ctx)

def my_print(s):
    print(s, end='', flush=True)



args = PIPELINE_ARGS(temperature = 1, top_p = 0.2, top_k = 0, # top_k = 0 then ignore
                     alpha_frequency = 0.5,
                     alpha_presence = 0.5,
                     alpha_decay = 0.998, # gradually decay the penalty
                     token_ban = [0], # ban the generation of some tokens
                     token_stop = [0,1], # stop generation whenever you see any token here
                     chunk_len = 256) # split input into chunks to save VRAM (shorter -> slower)

pipeline.generate(ctx, token_count=1000, args=args, callback=my_print,state=states_value)
print('\n')

The final printed input and output:

yueyulin/entity_type_extraction

This is a state for rwkv6_7b_v2.1 that generates entity_types given domain, expert role in this domain and specific tasks that this export can do that are generated from persona_domain agent.

Please refer to the following demo as test code:

The final printed input and output:

yueyulin/
entity_type_extraction